You can use artificial intelligence in two ways: pay per token for an API such as OpenAI, Anthropic Claude or Google Gemini, or rent a server with a GPU and run open models yourself. We compare cost, data confidentiality, latency, control over the model and answer quality.
| Criterion | Dedicated GPU server | AI API |
|---|---|---|
| Cost model | Fixed, monthly or annual | Variable, per million tokens |
| At low volume | Expensive per unit of use | Cheap, you pay only for what you use |
| At high, constant volume | Cost per request falls | The bill grows linearly |
| Where the data sits | On your server, in the EU | At the API provider, as per contract |
| Available models | Open: Llama, Mistral, Qwen | Frontier, closed-source |
| Peak quality | Good on targeted tasks | The highest available |
| Latency | Constant, no external queues | Variable, depends on the provider |
| Control over the model | Total: version, fine-tuning | The provider retires versions |
| Administration | Drivers, serving, monitoring | No infrastructure of your own |
Begin with real monthly consumption, counted in tokens. Say 100 million input tokens and 20 million output tokens a month: that is about USD 400 on Claude Sonnet 5, about USD 440 on Gemini 3.1 Pro Preview and about USD 2,000 on GPT-6 Astra. Set that number against the monthly subscription of a GPU server on the pricing page. When the API bill keeps nearing the server cost and an open model can do the job, the server wins. When volume swings or the task calls for a frontier model, stay with the API.
The memory on the card decides which models you can serve. As a rule of thumb, the weights take the parameter count times the bytes per parameter, and the conversation context needs extra room on top. An 8-billion-parameter model at 16-bit precision needs about 16 GB, so it fits the RTX PRO 4000 with 24 GB. A 70-billion-parameter model quantised to 4 bits needs about 35 GB for the weights alone, which calls for the RTX PRO 6000 with 96 GB. That card also leaves space for long contexts and many users at once.
With your own GPU server, text, documents and answers stay inside infrastructure in Frankfurt. No external retention policy applies to them, and the data processing agreement is signed with 4hosting. API vendors give guarantees as well: OpenAI offers EU regional processing for some models, at a 10 % higher price on the newest ones. What a server adds is control: you pick the model version, freeze it, and can train it further on your own data.
A translation bureau handles contracts and medical documents daily, none of which may reach third parties. It runs an open model on a server with an RTX PRO 6000 in an EU data centre, at a fixed recurring cost. With high and steady volume, that beats paying per token.
A shop is piloting a virtual assistant that gets a few hundred questions a day. Volume is low and seasonal, and the answers need sound reasoning. At that scale a pay-per-token API costs little and requires no infrastructure of its own.
An accounting SaaS sorts thousands of documents a day with an open model on an RTX PRO 4000. The occasional complex question from a user goes to a frontier API instead. Every task runs where cost and quality balance best.
Yes, for the smaller variants. Open models of up to roughly 8 billion parameters run comfortably at 16-bit precision in the 24 GB of memory. Models with tens of billions of parameters need aggressive quantisation or the RTX PRO 6000 with 96 GB.
Yes, and it is often the best setup. Repetitive, high-volume tasks run on the open model hosted on your server at a fixed cost. Rare requests that need complex reasoning go to a frontier API, which you pay for only when it is used.
No. 4hosting GPU servers are billed monthly or annually. For a one-off training run of a few hours, a service with hourly billing may fit better. A dedicated server pays off when the model runs continuously, in production.
An API wins at low volume and for frontier models; a dedicated GPU server wins at high, steady volume and when data must not leave your control.
